Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

• URL: http://arxiv.org/abs/2103.10245v1
• Date: Tue, 16 Mar 2021 23:39:33 GMT
• Title: Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
• Authors: Ashish Rana, Avleen Malhi
• Abstract summary: This study focuses on creating risk prone scenarios with heavy traffic and unexpected random behavior for creating better model-free learning agents. We generate multiple autonomous driving scenarios by creating new custom Markov Decision Process (MDP) environment iterations in highway-env simulation package. We train model free learning agents with supplement information of risk prone driving scenarios and compare their performance with baseline agents.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation environments are good for learning different driving tasks like lane changing, parking or handling intersections etc. in an abstract manner. However, these simulation environments often restrict themselves to operate under conservative interactions behavior amongst different vehicles. But, as we know that the real driving tasks often involves very high risk scenarios where other drivers often don't behave in the expected sense. There can be many reasons for this behavior like being tired or inexperienced. The simulation environments doesn't take this information into account while training the navigation agent. Therefore, in this study we especially focus on systematically creating these risk prone scenarios with heavy traffic and unexpected random behavior for creating better model-free learning agents. We generate multiple autonomous driving scenarios by creating new custom Markov Decision Process (MDP) environment iterations in highway-env simulation package. The behavior policy is learnt by agents trained with the help from deep reinforcement learning models. Our behavior policy is deliberated to handle collisions and risky randomized driver behavior. We train model free learning agents with supplement information of risk prone driving scenarios and compare their performance with baseline agents. Finally, we casually measure the impact of adding these perturbations in the training process to precisely account for the performance improvement attained from utilizing the learnings from these scenarios.

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